Understanding Gender Differences in Injecting‐Related Harms Among an Australian Sample of People Who Inject Drugs
Bibliographic record
Abstract
INTRODUCTION: People who inject drugs may experience several non-viral injecting-related injuries and diseases (IRID), including skin and soft tissue infection (SSTI) and venous disease, often resulting from bacteria introduced via unsafe injecting practices or environments. Women are overrepresented among those reporting multiple and recent IRID. However, limited evidence exists about how gender interacts with known IRID risk factors. METHODS: Surveys were conducted 2009-2023 with approximately 900 Australians who inject drugs per year (N = 7538 total). Participants self-reported past-month drug use behaviours and IRID experience. We conducted multivariable binary logistic regression to determine the relationship between gender and SSTI and venous disease. To examine whether gender uniquely affected specific injecting risk behaviours with respect to SSTI and venous disease, two interaction terms were separately added: (i) gender and injecting frequency; (ii) gender and reuse of one's needles. RESULTS: Surveys were completed by 5038 men and 2500 women. Past-month SSTI was reported by 8% of the sample (95% confidence interval 7%-9%), with a higher proportion among women (10%) than men (7%). Overall, 4% reported past-month venous disease (95% confidence interval 3%-4%), a higher proportion among women (5%) than men (3%). Examining both outcomes, no statistically significant interactions between gender and needle reuse or injecting frequency were found. DISCUSSION AND CONCLUSIONS: Despite no statistically significant interaction between gender and reuse or injecting frequency, our study demonstrates a gender difference in exposure to risk factors associated with SSTIs and venous disease. Interventions to reduce SSTI and venous disease, particularly those deemed safe and appropriate by women, are needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".